1 citations · 2 across the 13 of their papers we have counts for
4 papers · 1 filter
Incremental stability in and : classification and synthesis
Simon Kuang, Xinfan Lin
All Lipschitz dynamics with the weak infinitesimal contraction (WIC) property can be expressed as a Lipschitz nonlinear system in proportional negative feedback -- this statement,…
A Compression Perspective on Simplicity Bias
Tom Marty, Eric Elmoznino, Leo Gagnon +5
Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new light on this phenomenon through t…
Demystifying Lipschitz verification: positive matrices, negative results
Simon Kuang, Yuezhu Xu, S. Sivaranjani +1
The global Lipschitz constant of a neural network is related to robustness and generalization, yet unlike in many classical models, it is not plainly legible from the parameters. T…
Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
Simon Kuang, Xinfan Lin
We study the problem of propagating the mean and covariance of a general multivariate Gaussian distribution through a deep (residual) neural network using layer-by-layer moment mat…